Design and Implementation of a Hybrid Parallel Performance Measurement System

Abstract

Modern parallel performance measurement systems collect performance information either through probes inserted in the application code or via statistical sampling. Probe-based techniques measure performance metrics directly using calls to a measurement library that execute as part of the application. In contrast, sampling-based systems interrupt program execution to sample metrics for statistical analysis of performance. Although both measurement approaches are represented by robust tool frameworks in the performance community, each has its strengths and weaknesses. In this paper, we investigate the creation of a hybrid measurement system, the goal being to exploit the strengths of both systems and mitigate their weaknesses. We show how such a system can be used to provide the application programmer with a more complete analysis of their application. Simple example and application codes are used to demonstrate its capabilities. We also show how the hybrid techniques can be combined to provide real cross-language performance evaluation of an uninstrumented run for mixed compiled/interpreted execution environments (e.g., Python and C/C++/Fortran).